Tag: JMP Clinical

Analyzing adverse events using Bayesian hierarchical models

You may be asking yourself… “Two Bayesian posts in a row? What is going on?” Though my statistical training focused on Frequentist methodologies, I am a big believer in using whatever tools help me gain insight into the statistical problem I happen to be focusing on at the moment. Frequentist

The 2013 JMP Life Sciences European Roadshow

This May, JMP Life Sciences is going on the road in Europe to demo some of the new features that will be available in the upcoming releases of JMP Clinical 4.1 and JMP Genomics 6.1. The Roadshow is an excellent opportunity to hear about new functionality, ask questions or perhaps sneak

Truly efficient clinical reviews – an example

JMP Clinical 4.1 contains example data to help illustrate its new review functionality. This additional data is referred to as Nicardipine Early Snapshot, and includes Nicardipine data only through 01 Aug 1989. There are numerous changes to this data set: 11 subjects have yet to enroll in the trial, the

Truly efficient clinical reviews – it’s all about the keys

In last week’s post, we discussed some of the upcoming features of JMP Clinical 4.1 that identify new and modified records when clinical trial data is updated. These tools can greatly accelerate clinical reviews, allowing the clinician, statistician or data manager to focus exclusively on unreviewed records. Here we discuss

Truly efficient data reviews for clinical trials

Over the next few posts, I discuss the data review process for clinical trials and highlight some new features for JMP Clinical 4.1 that streamline this monumental endeavor. Ideally, the data from a clinical trial should be examined by as many eyes as possible – including data and protocol managers,

Predictive modeling in the life sciences

This past week, Nate Silver held an “Ask Me Anything” chat on Reddit. There were several very good questions, one of which I found particularly important as we begin the International Year of Statistics: “What is the biggest abuse of statistics”? To which Nate replied: “Overfitting.” This response is very

Identifying multivariate inliers and outliers

We’re nearing the end of this series of posts on fraud detection in clinical trials and some upcoming features of JMP Clinical 4.1 that help identify unusual observations. We’ve described how visit dates and measurements taken in the clinic can signify problems at the clinical site, and discussed how trial

Russ Wolfinger elected 2012 AAAS Fellow

Congratulations to Russ Wolfinger, PhD, who was elected a 2012 Fellow of the American Association for the Advancement of Science! Wolfinger was chosen for his "path-breaking statistical software used to analyze correlated data, promotion of statistical reasoning in science, and leadership in analysis of gene expression data," the AAAS said.